
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "49"
csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 22 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_
Found 22 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
            antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
        else:
            antnum = int(row['Ant'])
        if antnum != int(antenna):
            continue
        
        if np.issubdtype(type(row['Node']), np.integer):
            row['Node'] = str(row['Node'])
        if type(row['Node']) == str and row['Node'].isnumeric():
            row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


htmls_to_display = []
for am_html in auto_metric_htmls:
    html_to_display = ''
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line:
            htmls_to_display.append(html_to_display)
            break


for i, html_to_display in enumerate(htmls_to_display):
    if i == 100:
        break
    display(HTML(html_to_display))

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459998	not_connected	0.00%	0.00%	0.00%	0.00%	-	-	-0.422306	-0.046688	-0.496143	0.160671	-0.331791	-0.178620	0.146355	-0.093157	0.5302	0.5716	0.3723	nan	nan
2459997	not_connected	0.00%	0.00%	0.00%	0.00%	-	-	-0.454403	-0.557696	0.063950	-0.250453	-0.296478	-0.544159	0.226013	0.053987	0.5602	0.6024	0.3839	nan	nan
2459996	not_connected	0.00%	0.00%	0.00%	0.00%	-	-	-0.460398	-0.350218	0.544301	-0.321857	-0.619483	-0.685183	0.007637	0.798637	0.5603	0.5998	0.3950	nan	nan
2459995	not_connected	0.00%	0.00%	0.00%	0.00%	-	-	-0.311008	-0.129490	-0.757803	0.069525	0.020956	0.116735	0.024353	2.423420	0.5609	0.5978	0.3793	nan	nan
2459994	not_connected	0.00%	0.00%	0.00%	0.00%	-	-	-0.265544	-0.293863	-0.689296	-0.179737	0.053643	-0.708227	-0.297925	3.657036	0.5506	0.5887	0.3736	nan	nan
2459993	not_connected	0.00%	0.00%	0.00%	0.00%	-	-	0.289347	-0.006640	-0.978049	0.172533	1.627018	-0.105986	-0.229886	1.317910	0.5442	0.6043	0.3898	nan	nan
2459991	not_connected	0.00%	0.00%	0.00%	0.00%	-	-	0.215736	-0.408398	-0.148923	-0.236382	0.876459	-0.756130	-0.053814	0.132528	0.5486	0.5854	0.3835	nan	nan
2459990	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	0.193560	-0.238356	-0.164799	-0.289459	0.896402	-0.667283	0.205669	5.265605	0.5459	0.5863	0.3813	nan	nan
2459989	not_connected	0.00%	0.00%	0.00%	0.00%	-	-	0.056009	0.010566	-0.767541	0.195251	0.687493	-0.588985	-0.090604	3.914892	0.5447	0.5857	0.3833	nan	nan
2459988	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	0.117269	-0.256660	-0.244080	-0.301434	1.281810	-0.478502	0.650360	4.729693	0.5333	0.5735	0.3676	nan	nan
2459987	not_connected	0.00%	0.00%	0.00%	0.00%	-	-	-0.298655	-0.132487	-0.799150	0.166178	0.195524	-0.178204	1.682981	1.924559	0.5564	0.5962	0.3741	nan	nan
2459986	not_connected	0.00%	0.00%	0.00%	0.00%	-	-	0.348808	0.013578	-0.874606	-0.067584	1.064277	1.532325	1.200154	2.647379	0.5672	0.6063	0.3324	nan	nan
2459985	not_connected	0.00%	0.00%	0.00%	0.00%	-	-	-0.290391	-0.064499	-0.768315	0.028685	-0.321114	-0.597569	0.376592	1.306285	0.5487	0.5871	0.3770	nan	nan
2459984	not_connected	0.00%	0.00%	0.00%	0.00%	-	-	-0.986794	-0.297783	-1.308679	0.153845	0.508198	0.440683	1.464603	1.991001	0.5688	0.5980	0.3496	nan	nan
2459983	not_connected	0.00%	0.00%	0.00%	0.00%	-	-	0.132854	-0.209521	-0.799897	0.337741	1.151072	-0.397235	0.326471	-0.592560	0.5904	0.6384	0.3107	nan	nan
2459982	not_connected	0.00%	0.00%	0.00%	0.00%	-	-	0.049531	-0.621304	0.085376	-0.865348	-0.081640	-0.954800	1.047335	0.651746	0.6233	0.6516	0.3004	nan	nan
2459981	not_connected	0.00%	0.00%	0.00%	0.00%	-	-	0.095531	-0.091508	-1.038146	0.581713	1.208225	-0.315766	0.074871	0.095916	0.5426	0.5893	0.3807	nan	nan
2459980	not_connected	0.00%	0.00%	0.00%	0.00%	-	-	-0.077851	-0.587687	-0.362547	-0.898825	0.624877	1.315494	0.987796	1.333711	0.5984	0.6329	0.3149	nan	nan
2459979	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	0.216289	-0.332584	-1.125536	-0.510577	0.847097	-0.848817	0.753817	8.394044	0.5385	0.5838	0.3782	nan	nan
2459978	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	0.005198	-0.342304	-1.156313	-0.318773	0.941972	-0.820633	1.244240	11.356959	0.5363	0.5785	0.3852	nan	nan
2459977	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	0.225287	-0.588956	-0.318797	-1.007908	1.317686	-0.233969	2.990702	16.942456	0.5075	0.5501	0.3455	nan	nan
2459976	not_connected	100.00%	0.00%	0.00%	0.00%	-	-	0.117825	-0.429724	-0.393018	-0.982830	0.758836	-1.171126	-0.038478	9.894392	0.5482	0.5901	0.3802	nan	nan

Antenna Season Report Notebook¶

Antenna 49 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 49, Node N06:

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 49: 2459998

Antenna 49: 2459997

Antenna 49: 2459996

Antenna 49: 2459995

Antenna 49: 2459994

Antenna 49: 2459993

Antenna 49: 2459991

Antenna 49: 2459990

Antenna 49: 2459989

Antenna 49: 2459988

Antenna 49: 2459987

Antenna 49: 2459986

Antenna 49: 2459985

Antenna 49: 2459984

Antenna 49: 2459983

Antenna 49: 2459982

Antenna 49: 2459981

Antenna 49: 2459980

Antenna 49: 2459979

Antenna 49: 2459978

Antenna 49: 2459977

Antenna 49: 2459976

Antenna Season Report Notebook¶

Antenna 49 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 49, Node N06:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 49: 2459998

Antenna 49: 2459997

Antenna 49: 2459996

Antenna 49: 2459995

Antenna 49: 2459994

Antenna 49: 2459993

Antenna 49: 2459991

Antenna 49: 2459990

Antenna 49: 2459989

Antenna 49: 2459988

Antenna 49: 2459987

Antenna 49: 2459986

Antenna 49: 2459985

Antenna 49: 2459984

Antenna 49: 2459983

Antenna 49: 2459982

Antenna 49: 2459981

Antenna 49: 2459980

Antenna 49: 2459979

Antenna 49: 2459978

Antenna 49: 2459977

Antenna 49: 2459976

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶